Field of Invention
A method for adaptive conversation state management in a conversational interface for information retrieval where filtering operators can be dynamically applied to modify the conversation state is disclosed.
Description of Related Art and Context of the Invention
The filtering operators themselves are part of the conversation exchanges and are inferred from the exchanges. The conversation state space dynamically adapts to the filtering operators, expanding or pruning state, and adjusting weights of items in the conversation space, based on the operators. One method described in the present disclosure also implicitly flushes the state space annulling all applied filters, when it detects a conversation thread boundary. One method described in the present disclosure enables the conversation exchange to be closer in spirit to human interactions, where intent expression straddles across multiple exchanges and conversation threads are often seamlessly woven into a continuous flow. Furthermore, the embodiments of the present invention enable a natural correction method for user input errors, such as errors in speech recognition—these input errors can be corrected by users vocalizing filtering operations as part of the conversation exchanges with the information retrieval system.
Information retrieval process, in non-conversational interfaces, is often multistep, even when user intent is clear and unambiguous. This may be due to multiple factors, one of which is the potential set of matches to user intent may be large. In such cases, user typically culls the match space by specifying constraints or filters (e.g. adding more key words in Google search bar to an existing search query, turning on a filter to show only five-star rated products on a web site). At the other end of this spectrum of factors requiring multistep information retrieval, is the case where the matches are too few, from a choice perspective. In this case, user would broaden the intent by specifying broadening filters (e.g. turning on a filter that includes third party vendor products too in the search results on a commerce site).
Information retrieval in non-conversational interfaces is inherently multistep when user intent is not clear. The retrieval process is exploratory; where user not only applies culling, and expanding filters, but also traverses paths related to content that piques user's interest (e.g. traversing related items in a commerce site like amazon.com).
In all these non-conversational interfaces, user progressively applies filters in the content discovery process. User explicitly manages the filters to be applied for each step of the discovery process. Information retrieval systems that are session based, reset the filters on each session boundary, where a session could be defined, for example, as a new search input. In information retrieval systems that are not session based, users are burdened even more by having to selectively reset inapplicable filters across conversation threads.
People typically apply filters when they converse with each other, but the management of filters feels so much easier, than when interacting with non-conversation interfaces. This is perhaps because application of a filter is as simple as mentioning it as part of the conversation. Even more importantly, the state space of the topic being discussed smoothly adapts to the exchanges and evolves simultaneously in the minds of the participants engaged in the conversation, liberating them of the need to explicitly manage and synchronize the state space of conversation by reiterating or recalling. Additionally, when people converse, conversation thread boundaries do not require conscious resetting of filters; neither do people feel a tension that inapplicable filters are being transferred to a new thread. These are perhaps essential factors contributing to an engaging conversation and are often summed up in the words of a participant—“when X and I talk, we are in sync”. Needless to say, in the case of humans, it is not just the liberation from synchronizing conversation state space among participants, but the fact that the participants can also simultaneously distill concepts from the state space in the backdrop of their personal experiences and knowledge, and express contextually relevant thoughts within the conversation as ideas, opinions, counter-arguments etc. The liberation from synchronization of state space is the edifice. The exchange of thoughts relies on and wraps around building on this edifice.
Conversational systems that strive for the modest goal of emulating the edifice of human conversations by adaptively maintaining state of the conversation as filters are dynamically applied by user within the conversation, and implicitly recognizing conversation thread boundaries, to reset these filters, would go a long way in improving the user experience.
Speech recognition has finally reached a threshold opening up the possibility of conversational systems to become main stream, at least in limited use case scenarios. However, speech recognition still falters on accent variations, and mobile environments. Filtering operations are essential, particularly in mobile environments, where speech recognition partially succeeds, so user can apply filters, without having to repeat the expressed intent all over again.
In summary, the present disclosure describes methods of managing the state of a conversation in information retrieval systems, allowing the application of filters, as part of the conversation. The method further recognizes conversation thread boundaries and annuls the filters automatically without explicit user intervention. Methods described in the present disclosure are also applicable when speech recognition errors result in a response that partially satisfy user intent—user can speak filtering operations without having to repeat the original intent all over again.